Cross-Source Data Blocks for Decentralized Relationship Analysis
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Solution Overview
Problem
Existing data management systems struggle with managing and analyzing massive amounts of decentralized data, particularly in cloud computing environments, as they often lack the ability to efficiently store and restructure data to uncover new relationships and correlations, leading to incomplete information and high operational costs.
Innovation Solution
The enterprise data processing module (EDP) collects data from various sources, analyzes relationships using correlation intensity algorithms, and stores data in a way that preserves correlation information, allowing users to quickly explore and interact with data in real-time, while maintaining accessibility and flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored in a decentralized manner across multiple servers, then storage capacity and accessibility are improved, but the ability to analyze relationships and correlations between data is lost
Solution Approach 1:
The patent implements a nested data structure where data blocks contain both the actual data and embedded relationship metadata. Each data block is self-contained with correlation information nested within it, allowing the system to maintain decentralized storage while preserving relationship data within each unit. This nesting approach enables data to be stored distributedly across multiple servers while each block independently carries its relationship context.
Solution Approach 2:
The patent introduces relationship metadata as an intermediary layer that bridges decentralized data blocks. This metadata layer captures correlation information between data elements and attaches it to relevant data blocks, serving as a mediator that preserves relationship context without requiring centralized storage. The intermediary metadata enables analysis of relationships while data remains distributed.
2Productivity
If data is formatted for task-specific computations, then computation efficiency is improved, but flexibility to re-analyze data for new values is reduced
Solution Approach 1:
The patent implements dynamic data blocks that can adapt their structure and content based on analysis needs. Data blocks are not statically formatted for single purposes but can be dynamically reconfigured to support different analysis tasks. The relationship metadata within each block enables flexible querying and re-analysis without requiring data restructuring, allowing the same decentralized data structure to serve multiple analytical purposes efficiently.
Solution Approach 2:
The patent creates universal data blocks that can serve multiple functions simultaneously. Each data block is designed to handle various types of computations and analyses through its embedded relationship metadata, eliminating the need for separate formatted versions for different tasks. This multi-functional design allows efficient computation for task-specific operations while maintaining flexibility for re-analysis of new values.
3Loss of information
If all data is retrieved and organized locally for analysis, then complete information is obtained, but time and computational resources are consumed
Solution Approach 1:
The patent extracts only the necessary relationship metadata from decentralized data blocks and brings it locally for analysis, rather than retrieving all underlying data. The system selectively extracts correlation information embedded in data blocks to perform local analysis, obtaining complete relationship information without the overhead of moving and organizing entire datasets. This extraction approach maintains information completeness while dramatically reducing processing time and resource consumption.
4Loss of information
If data is continuously re-structured to find new values, then new insights are discovered, but operational costs increase
Solution Approach 1:
The patent performs preliminary organization of relationship metadata during data ingestion and storage phases. Relationship correlations are pre-computed and embedded in data blocks before analysis queries are executed. This preliminary action eliminates the need for continuous expensive re-structuring operations to discover new values, as the relationship infrastructure is already in place to support efficient querying and insight discovery across multiple analysis tasks.
Data Source
AI summary
An enterprise data processing module and method are described herein. The enterprise data processing module comprises at least one collector and at least one analyzer. The collectors may be operable to collect data pieces from a plurality of data sources. The analyzers may be operable to analyze the collected data pieces to determine cross-source relationships that exist between the data pieces collected from the plurality of sources. The analyzed data pieces may be stored in one or more big-data databases as blocks of data according to the cross-source relationships.